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| Content Provider | IET Digital Library |
|---|---|
| Author | Wei, Kecheng Wu, Jie Ma, Wenbo Li, Huangchou |
| Abstract | Unmanned aerial vehicle (UAV) is a power-driven aircraft that is unmanned and reusable. The purpose of this study is to accurately estimate the state of charge (SOC) of lithium-ion batteries for UAVs. A support vector machine (SVM) method, SVM is a type of learning machine based on statistical learning, is used as the input variable of the battery charging discharge data (current, voltage and temperature). The kernel of the radial basis function is the best kernel of authors’ experiment, where the C, ν and g values are 1, 0.012 and 0.0125, respectively. The experimental results from the lithium-ion battery data at NASA Ames Prognostics Center of Excellence demonstrate the potential application of the proposed method as an effective tool for battery SOC prediction. The accuracy of the whole experiment is 98.42%. Mean-squared error is 1.783%. The experimental results show that the model has higher accuracy in predicting the discharge capacity of lithium battery SOC-training samples. |
| Starting Page | 9133 |
| Ending Page | 9136 |
| Page Count | 4 |
| Volume Number | 2019 |
| e-ISSN | 20513305 |
| Issue Number | Issue 23, Dec (2019) |
| Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/joe/2019/23 |
| Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/10.1049/joe.2018.9201 |
| Journal | The Journal of Engineering |
| Publisher | The Institution of Engineering and Technology |
| Publisher Date | 2019-03-25 |
| Access Restriction | Open |
| Rights License | Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/) |
| Subject Keyword | Aerospace Control Autonomous Aerial Vehicle Battery Charging Discharge Data Battery SOC Prediction Interpolation And Function Approximation Knowledge Engineering Technique Learning in AI Lithium-ion Battery Machine Learning Mean Square Error Method Mean-squared Error Mobile Robots NASA Ames Prognostics Center of Excellence Demonstrate Neural Computing Technique Numerical Analysis Numerical Approximation And Analysis Power Engineering Computing Radial Basis Function Radial Basis Function Network Secondary Cell State of Charge Prediction Statistical Learning Support Vector Machine Support Vector Machine Method SVM Method UAV |
| Content Type | Text |
| Resource Type | Article |
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